Earticle

PID controllers are widely used in industrial plants because it is simple and robust. So the control engineers are on look for tuning procedures. This paper gives the review of some soft computing techniques which are used for PID tuning. Soft computing techniques make easy of the tuning of parameters of controller to get desired response.

In engineering, solving divergence and long solving time often occurs when solving large complex dynamic equations. Comparative analysis of Newmark and Rungekutta method fora complex dynamic model canprovidea theory basis for the selection of numerical integration method when solving complex dynamic equation.The comparative analysis on the solving precision and solving time by Newmark method andRungekutta method for calculating a same complex dynamic equation is carried out. The calculation results show that the response results by these two methods are in strong consistency. But the Newmark method shortens the solving time by at least 3 orders comparing that of Rungekutta method. Furthermore, the response displacement by Newmark method in different integration steps is obtained. The results indicate that the response results become stable with integration step continues reducing under 0.1.

General dense matrix multiplication operation (GEMM) has a great impact on the performance of supercomputers. The typical blocking algorithm of GEMM divides the operation in to several levels. Because of the limitation of data transmission, previous works only focuses on optimizing the lowest level of the GEMM based on general-purpose processors. However, the Decoupled Access/Execute architecture (DAE) processor has enhanced the ability of data fetching. This paper will introduce several methods for optimizing GEMM based on a DAE processor. The Execute Processors (EP) of the platform is made up by Direct Register Access (DRA) and Direct Cache Access (DCA), which can be used to manage the data transport between registers, cache and memory. This paper pays more attention on optimizing the high levels of the blocking GEMM. The GEMM kernel based on the DAE processor was divided into 4 levels, and several levels of the new algorithm are capable to self-adjust. And the performance of our algorithm was effectively improved.

In recent years, applications of cloud services have been increasingly expanded. Cloud services, are distributed infrastructures which develop the communication and services. Auto scaling is one of the most important features of cloud services which dedicates and retakes the allocated dynamic resource in proportion to the volume of requests. The Scaling tries to utilize maximum power of the available resources also to use idle resources, in order to maximize the efficiency or shutdown unnecessary resources to reduce the cost of running requests. In this paper, we have suggested an approach based on learning automata for resource auto-scaling, in order to manage and optimize factor cost. Results of simulation show that proposed approach has been able to optimize cost compared to the other approaches.

This paper introduces a new multi-view and controllable community-uncovering algorithm, an achievement of improving PageRank algorithm and Spin-glass model, which can avoid the overlapping community structure in the process of detecting communities by means of other algorithms and also helps to improve the usual community-expansion model.The process of uncovering communities by the introduced algorithms can be divided into three steps: first, identifying the nuclear one among nodes ranked by the advanced PageRank algorithms; Second, through using multi-view recognition modularity provided by Potts spin-glass model, optimizing the expansion model of local community that is found by applying the improved Iterative Greedy algorithm to eliminate the traditional modularity’s limits in the resolution limit and the following negative effects. Finally, grasping the overlapping structure and notes carefully. By analyzing and comparing the two results of respectively using PRSGMFCA and traditional technical schemes in both computer simulation network and the real network, it proves that the former enjoys stronger stability and higher accuracy than the latter, and its computation complexity is also acceptable.

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of simulation experiments which compare to the result of Z-N optimization and evolution of genetic algorithm optimization and evolution of ant colony system optimization, it has more excellent performance in finding best solution and convergence, the PID parameters also have optimality, system possesses dynamic controlling and performance. The experiments show that this method has its practical value on controlling other objection and process.

The aim of this paper is to explain the way to scale back the dimension of an influence system stabilizer (PSS) supported H∞ management theory. In recent years, sturdy PSS styles that adopt associate in nursing H∞ controller are investigated so as to ensure the performance once the state of the system configuration and power flow modification. However the H∞ controller has not been wide adopted into sensible use attributable to the tangled nature of its theory and structure. We tends to think about the H∞ management downside below the condition that PSS structure is mounted to be a lead-lag compensator. Infinity norm of transfer function from disturbance to output is subjected to be minimized via searching and evolutionary computation. The resulted optimal parameters make the system stable and also guarantee robust performance. We applied the evolutionary robust controller to a pneumatic servo system. For performances comparison, three controllers; PID with derivative first order filter controller, PI controller and H- loop shaping controller are investigated. We tends to optimize the parameters of the Lead-Lag PSS by a genetic algorithm program that has associate in nursing analysis perform that takes under consideration a closed-loop system H∞ norm and a desired response. During this manner, we tends to design a PSS that encompasses a standard managementler structure and guarantees its control performance.

Computer network virus has been manufactured in bulk, which can affect the normal work and study of people, and therefore the defense and detection of computer network virus should be concerned, advanced defense ability of computer network virus under the environment of computer internet can be improved. Firstly, the basic characteristics of computer network virus are analyzed. Secondly, main computer network virus propagation models are studied. Thirdly, active defense technology and detection model of computer network virus are established based on supported vector machine, and corresponding algorithm procedure is constructed. Finally, the simulation experiments are carried out, and results show that the detection model can obtain better detection effect of the computer network virus.